In applications where large, heavy, and/or expensive inertial measurement units (IMUs) are not practical, such as in mobile telephones, portable navigation devices, other handheld devices, and the like, magnetometers combined with gyroscopes and accelerometers are often used. These are typically microelectromechanical systems (MEMS) based, low cost, very compact and consume little power. However, limitations of MEMS technology, especially the typical devices found in mobile devices, include stability and noise issues. Bearing is typically determined using magnetometers in combination with Global Navigation Satellite Systems (GNSS) receivers, so magnetic distortions can cause false readings while the loss of signal from GNSS when travelling in tunnels or buildings can results in a loss of position and orientation. For applications such as dead reckoning and for navigation in GNSS denied environments, the gyroscope is an ideal solution since it does not suffer from magnetic distortions or anomalies. However, MEMS gyroscopes are very noisy and suffer from environmentally induced errors.
Typical approaches to extracting rotational information, such as the Earth's rotation, from a MEMS gyroscope include linear-long term analysis (i.e. the unit must be kept static for many minutes or hours in order to perform the measurement). Many of these techniques use recursive filtering techniques such as a Kalman filter, while more recent approaches include artificial analysis approaches such as Markov Decision Process and Particle Filtering. While the aforementioned approaches typically rely on the system remaining linear, there are approaches to allow these approaches to give good results in a non-linear environment. One such approach is the Extended Kalman Filter. The effectiveness of these approaches can be improved by reducing the noise on the sensors before applying a filter or subsequent noise reduction technique. Employing more than one sensor, such as a MEMS gyroscope and averaging the readings from each sensor for a given sample period will reduce the noise.
The current state of the art is to employ either very expensive and bulky laser ring gyroscopes or low costs MEMS sensors and apply long term averaging and/or using Kalman filters (including extended Kalman filters) and particle filters. The issue with the latter approach is that it only really works when the filter is stationary and does not function well in a dynamic environment. This is due to the long term averaging needed in order to, for example, detect and measure the Earth's rotation.
The most commonly used approach to reduce the noise on the gyroscope is a complimentary filter which combines the output of the gyroscope with an accelerometer. These two sensors work to filter each other. However, this technique will not facilitate the measurement of the Earth's rotation with currently available MEMS sensors.